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In a tidy dataset, observations are organised into rows, and they have an identifier. The base R data.frame comes with row.names(). The modernised tibble allows the easy addition of the row identifier (as a primary key to the dataset’s tabular form). The data.table makes indexing more efficient. The ts time-series object or its more modern tsibble form adds clear identifiers for the time dimensions of the data.

The usability of a dataset is increased if we can easily and unambiguously add new observations with rbind(...) or a similar function. Without logical or semantic confusion, this is only possible if the binding of new observations uses the same identifiers. Eventually, the dataset will be the most usable if the identifiers are global, persistent and unique, enabling data linking via the web.

Recalling the first observation from the Example 9 of the RDF Data Cube Vocabulary definition, the eg: abbreviation is a shorthand of a Uniform Resource Identifier (URI) or Internationalized Resource Identifier (IRI) in the domain. Normally, this observation identifier should resolve to a globally unique identifier which is available on the World Wide Web as a human and machine-readable identifier (with optional description.)

# Example 9 of the RDF Data Cube Vocabulary definition

eg:o1 a qb:Observation;
    qb:dataSet  eg:dataset-le1 ;
    eg:refArea                 ex-geo:newport_00pr ;                  
    eg:refPeriod               <> ;
    sdmx-dimension:sex         sdmx-code:sex-M ;
    sdmx-attribute:unitMeasure <> ;
    eg:lifeExpectancy          76.7 ;

To go back to the iris dataset: the observation (row) identifier is certainly not globally unique because the row identifiers 1, 2, … 150 are present by default in any R data frame with 150 rows. What would make them unique if the eg: shorthand would resolve to a unique identifier. The simplest way to create such a unique identifier is to derive the root of the observation (row) identifier from a globally unique identifier of the dataset.

eg_iris <-iris
row.names(eg_iris) <- paste0("eg:o", row.names(iris))
#>       Sepal.Length Sepal.Width Petal.Length Petal.Width Species
#> eg:o1          5.1         3.5          1.4         0.2  setosa
#> eg:o2          4.9         3.0          1.4         0.2  setosa
#> eg:o3          4.7         3.2          1.3         0.2  setosa
#> eg:o4          4.6         3.1          1.5         0.2  setosa
#> eg:o5          5.0         3.6          1.4         0.2  setosa
#> eg:o6          5.4         3.9          1.7         0.4  setosa

We placed a copy of the (semantically enhanced) version of the famous iris dataset with the digital object identifier (DOI) 10.5281/zenodo.10396807 to the Zenodo data repository that promises many decades of availability for this copy. The DOI identifier is designed so that the URI, as a URL, i.e., universal resource locator, dereferences to the actual location of the dataset as a resource.

row.names(eg_iris) <- paste0("", row.names(iris))
#>                                            Sepal.Length Sepal.Width
#>          5.1         3.5
#>          4.9         3.0
#>          4.7         3.2
#>          4.6         3.1
#>          5.0         3.6
#>          5.4         3.9
#>                                            Petal.Length Petal.Width Species
#>          1.4         0.2  setosa
#>          1.4         0.2  setosa
#>          1.3         0.2  setosa
#>          1.5         0.2  setosa
#>          1.4         0.2  setosa
#>          1.7         0.4  setosa

Replacing the eg: shorthand or prefix to uniquely identifies each observation (row) in our semantically enriched version of the iris dataset.

The dataset package aims to add this functionality to R data frames to be serialised into a format to the semantic web or web of data. While the addition can be made with 0.2.9, we will develop more helper functions after user feedback.